Corpus ID: 6551701

Text Modeling using Unsupervised Topic Models and Concept Hierarchies

@article{Chemudugunta2008TextMU,
  title={Text Modeling using Unsupervised Topic Models and Concept Hierarchies},
  author={Chaitanya Chemudugunta and Padhraic Smyth and Mark Steyvers},
  journal={ArXiv},
  year={2008},
  volume={abs/0808.0973}
}
  • Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers
  • Published in ArXiv 2008
  • Computer Science
  • Statistical topic models provide a general data-driven fra mework for automated discovery of highlevel knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set, the interpretabil ity of the learned topics is not always ideal. Human-defined concepts, on the other hand, tend to be s emantically richer due to careful selection of words to define concepts but they tend not to cover t he themes in a data set exhaustively… CONTINUE READING

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